{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Subsurface Data Analytics \n",
    "\n",
    "### Naive Bayes Classification for Subsurface Data Analytics in Python \n",
    "\n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PGE 383 Exercise: Naive Bayes Classification for Subsurface Data Analytics in Python \n",
    "\n",
    "Here's a simple workflow, demonstration of naive Bayes classification for subsurface modeling workflows. This should help you get started with building subsurface models that with predictions based on multiple sources of information.\n",
    "\n",
    "This method is great as it builds directly on our knowledge Bayesian statistics to provide a simple, but flexible classification method.\n",
    "\n",
    "#### Bayesian Updating\n",
    "\n",
    "The naive Bayes classifier is based on the conditional probability of a category, $k$, given $n$ features, $x_1, \\dots , x_n$.  \n",
    "\n",
    "\\begin{equation}\n",
    "p(C_k | x_1, \\dots , x_n)\n",
    "\\end{equation}\n",
    "\n",
    "we can solve this with Bayesian updating:\n",
    "\n",
    "\\begin{equation}\n",
    "p(C_k | x_1, \\dots , x_n) = \\frac{p(x_1, \\dots , x_n | C_k) p(C_k)}{p(x_1, \\dots , x_n)}\n",
    "\\end{equation}\n",
    "\n",
    "let's combine the likelihood and prior for the momment:\n",
    "\n",
    "\\begin{equation}\n",
    "p(x_1, \\dots , x_n | C_k) p(C_k) = p(x_1, \\dots , x_n, C_k)\n",
    "\\end{equation}\n",
    "\n",
    "we can exand the full joint distribution recursively as follows:\n",
    "\n",
    "\\begin{equation}\n",
    "p(x_1, \\dots , x_n, C_k)\n",
    "\\end{equation}\n",
    "\n",
    "expansion of the joint with the conditional and prior\n",
    "\n",
    "\\begin{equation}\n",
    "p(x_1 | x_2, \\dots , x_n, C_k) p(x_2, \\dots , x_n, C_k)\n",
    "\\end{equation}\n",
    "\n",
    "continue recursively expanding\n",
    "\n",
    "\\begin{equation}\n",
    "p(x_1 | x_2, \\dots , x_n, C_k) p(x_2 | x_3, \\dots , x_n, C_k) p(x_3, \\dots , x_n, C_k)\n",
    "\\end{equation}\n",
    "\n",
    "we can generalize as\n",
    "\n",
    "\\begin{equation}\n",
    "p(x_1 | x_2, \\dots , x_n, C_k) p(x_2 | x_3, \\dots , x_n, C_k) p(x_3 | x_4, \\dots , x_n, C_k) \\ldots (x_{n-1} | x_n, C_k)  (x_{n} | C_k) p(C_k)\n",
    "\\end{equation}\n",
    "\n",
    "#### Naive Bayes Approach\n",
    "\n",
    "The likelihood, conditional probability with the joint conditional is difficult to calculate.  It requires information about the joint relationship between $x_1, \\dots , x_n$ features. As $n$ increases this requires a lot of data to inform the joint distribution.\n",
    "\n",
    "With the naive bayes approach we make the 'naive' assumption that the features are all **conditionally independent**.  This entails:\n",
    "\n",
    "\\begin{equation}\n",
    "p(x_i | x_{i+1}, \\ldots , x_n, C_k) = p(x_i | C_k)\n",
    "\\end{equation}\n",
    "\n",
    "for all $i = 1, \\ldots, n$ features.\n",
    "\n",
    "We can now solve for the needed conditional probability as:\n",
    "\n",
    "\\begin{equation}\n",
    "p(C_k | x_1, \\dots , x_n) = \\frac{p(C_k) \\prod_{i=1}^{n} p(x_i | C_k)}{p(x_1, \\dots , x_n)}\n",
    "\\end{equation}\n",
    "\n",
    "We only need the prior, $p(C_k)$, and a set of conditionals, $p(x_i | C_k)$, for all predictor features, $i = 1,\\ldots,n$ and all categories, $k = 1,\\ldots,K$.\n",
    "\n",
    "The evidence term, $p(x_1, \\dots , x_n)$, is only based on the features $x_1, \\dots , x_n$; therefore, is a constant over the categories $k = 1,\\ldots,n$. \n",
    "\n",
    "* it ensures closure - probabilities over all categories sum to one \n",
    "\n",
    "* we simply standardize the numerators to sum to one over the categories.  \n",
    "\n",
    "The naive Bayes approach is:\n",
    "\n",
    "* simple to understand, builds on fundamental Bayesian statistics\n",
    "\n",
    "* pratical even with small datasets since with the conditional independence we only need to estimate simple conditional distributions \n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - [unconv_MV_v4.csv](https://git.io/fhHLT).\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Import Required Packages\n",
    "\n",
    "Let's import the GeostatsPy package.  I actually don't use it in this workflow, but just incase.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB                            # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats                      # GSLIB methods convert to Python        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                                          # ndarrys for gridded data\n",
    "import pandas as pd                                         # DataFrames for tabular data\n",
    "import os                                                   # set working directory, run executables\n",
    "import matplotlib.pyplot as plt                             # for plotting\n",
    "from scipy import stats                                     # summary statistics\n",
    "import math                                                 # trig etc.\n",
    "from sklearn.model_selection import train_test_split        # train and test split\n",
    "from sklearn.naive_bayes import GaussianNB                  # naive Bayes model and prediction\n",
    "from sklearn import metrics                                 # measures to check our modelscmap\n",
    "cmap = plt.cm.inferno                                       # inferno color map!\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")                           # suppress warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  \n",
    "\n",
    "#### Declare functions\n",
    "\n",
    "Let's define a couple of functions to streamline plotting correlation matrices and visualization of a decision tree regression model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_corr(dataframe,size=10):                               # plots a graphical correlation matrix \n",
    "    corr = dataframe.corr()\n",
    "    fig, ax = plt.subplots(figsize=(size, size))\n",
    "    im = ax.matshow(corr,vmin = -1.0, vmax = 1.0,cmap=cmap)\n",
    "    plt.xticks(range(len(corr.columns)), corr.columns);\n",
    "    plt.yticks(range(len(corr.columns)), corr.columns);\n",
    "    plt.colorbar(im, orientation = 'vertical')\n",
    "    plt.title('Correlation Matrix')\n",
    "    \n",
    "def visualize_model(model,xfeature,x_min,x_max,yfeature,y_min,y_max,response,z_min,z_max,title,):# plots the data points and the decision tree prediction \n",
    "    cmap = plt.cm.plasma\n",
    "    xplot_step = (x_max - x_min)/300.0; yplot_step = (y_max - y_min)/300.0 # resolution of the model visualization\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, xplot_step), # set up the mesh\n",
    "                     np.arange(y_min, y_max, yplot_step))\n",
    "    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])      # predict with our trained model over the mesh\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    cs = plt.contourf(xx, yy, Z, cmap=cmap,vmin=z_min, vmax=z_max, levels = 50) # plot the predictions\n",
    "    # add the data values as a colored by response feature scatter plot\n",
    "    im = plt.scatter(xfeature,yfeature,s=None, c=response, marker=None, cmap=cmap, norm=None, vmin=z_min, vmax=z_max, alpha=0.8, linewidths=0.3, edgecolors=\"black\")\n",
    "    plt.title(title)                                       # add the labels\n",
    "    plt.xlabel(xfeature.name); plt.ylabel(yfeature.name)\n",
    "    plt.xlim([x_min,x_max]); plt.ylim([y_min,y_max])\n",
    "    cbar = plt.colorbar(im, orientation = 'vertical')      # add the color bar\n",
    "    cbar.set_label(response.name, rotation=270, labelpad=20)\n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "    return(plt)\n",
    "    \n",
    "def visualize_model_prob(model,xfeature,x_min,x_max,yfeature,y_min,y_max,response,title,):# plots the data points and the prediction probabilities \n",
    "    n_classes = 10\n",
    "    cmap = plt.cm.inferno\n",
    "    xplot_step = (x_max - x_min)/300.0; yplot_step = (y_max - y_min)/300.0 # resolution of the model visualization\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, xplot_step), # set up the mesh\n",
    "                     np.arange(y_min, y_max, yplot_step))\n",
    "    \n",
    "    z_min = 0.0; z_max = 1.0\n",
    "    Z = model.predict_proba(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z1 = Z[:,0].reshape(xx.shape); Z2 = Z[:,1].reshape(xx.shape)\n",
    "    \n",
    "    plt.subplot(121)\n",
    "    cs1 = plt.contourf(xx, yy, Z1, cmap=plt.cm.YlOrBr,vmin=z_min, vmax=z_max, levels=np.linspace(z_min, z_max, 100))\n",
    "    im = plt.scatter(xfeature,yfeature,s=None, c=response, marker=None, cmap=plt.cm.Greys, norm=None, vmin=z_min, vmax=z_max, alpha=0.8, linewidths=0.3, edgecolors=\"black\")\n",
    "    plt.title(title + ' Probability of Low Production')\n",
    "    plt.xlabel(xfeature.name)\n",
    "    plt.ylabel(yfeature.name)\n",
    "    cbar = plt.colorbar(cs1, orientation = 'vertical')\n",
    "    cbar.set_label('Probability', rotation=270, labelpad=20)\n",
    "    \n",
    "    plt.subplot(122)\n",
    "    cs2 = plt.contourf(xx, yy, Z2, cmap=plt.cm.YlOrBr,vmin=z_min, vmax=z_max, levels=np.linspace(z_min, z_max, 100))\n",
    "    im = plt.scatter(xfeature,yfeature,s=None, c=response, marker=None, cmap=plt.cm.Greys, norm=None, vmin=z_min, vmax=z_max, alpha=0.8, linewidths=0.3, edgecolors=\"black\")\n",
    "    plt.title(title + ' Probability of High Production')\n",
    "    plt.xlabel(xfeature.name)\n",
    "    plt.ylabel(yfeature.name)\n",
    "    cbar = plt.colorbar(cs2, orientation = 'vertical')\n",
    "    cbar.set_label('Probability', rotation=270, labelpad=20)\n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)\n",
    "    plt.show()\n",
    "       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.chdir(r\"C:\\Users\\pm27995\\Downloads\")                                       # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will have to update the part in quotes with your own working directory and the format is different on a Mac (e.g. \"~/PGE\").  \n",
    "\n",
    "#### Read the data table\n",
    "\n",
    "First copy the \"unconv_MV.csv\" comma delimited file from https://github.com/GeostatsGuy/GeoDataSets to your working directory, then run this command to read the file into a DataFrame object (part of Pandas package).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#my_data = pd.read_csv(\"unconv_MV_v4.csv\")                   # load the comma delimited data file\n",
    "my_data = pd.read_csv(r\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/unconv_MV_v4.csv\") # load from Dr. Pyrcz's GitHub repo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the first several rows of our data stored in a DataFrame so we can make sure we successfully loaded the data file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Well</th>\n",
       "      <th>Por</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>Brittle</th>\n",
       "      <th>TOC</th>\n",
       "      <th>VR</th>\n",
       "      <th>Prod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>12.08</td>\n",
       "      <td>2.92</td>\n",
       "      <td>2.80</td>\n",
       "      <td>81.40</td>\n",
       "      <td>1.16</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1695.360819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>12.38</td>\n",
       "      <td>3.53</td>\n",
       "      <td>3.22</td>\n",
       "      <td>46.17</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.88</td>\n",
       "      <td>3007.096063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>14.02</td>\n",
       "      <td>2.59</td>\n",
       "      <td>4.01</td>\n",
       "      <td>72.80</td>\n",
       "      <td>0.89</td>\n",
       "      <td>2.72</td>\n",
       "      <td>2531.938259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>17.67</td>\n",
       "      <td>6.75</td>\n",
       "      <td>2.63</td>\n",
       "      <td>39.81</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.88</td>\n",
       "      <td>5288.514854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>17.52</td>\n",
       "      <td>4.57</td>\n",
       "      <td>3.18</td>\n",
       "      <td>10.94</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2859.469624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>14.53</td>\n",
       "      <td>4.81</td>\n",
       "      <td>2.69</td>\n",
       "      <td>53.60</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.67</td>\n",
       "      <td>4017.374438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>13.49</td>\n",
       "      <td>3.60</td>\n",
       "      <td>2.93</td>\n",
       "      <td>63.71</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.85</td>\n",
       "      <td>2952.812773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>11.58</td>\n",
       "      <td>3.03</td>\n",
       "      <td>3.25</td>\n",
       "      <td>53.00</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.93</td>\n",
       "      <td>2670.933846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>12.52</td>\n",
       "      <td>2.72</td>\n",
       "      <td>2.43</td>\n",
       "      <td>65.77</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2474.048178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>13.25</td>\n",
       "      <td>3.94</td>\n",
       "      <td>3.71</td>\n",
       "      <td>66.20</td>\n",
       "      <td>1.14</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2722.893266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>15.04</td>\n",
       "      <td>4.39</td>\n",
       "      <td>2.22</td>\n",
       "      <td>61.11</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.77</td>\n",
       "      <td>3828.247174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>16.19</td>\n",
       "      <td>6.30</td>\n",
       "      <td>2.29</td>\n",
       "      <td>49.10</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.86</td>\n",
       "      <td>5095.810104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>16.82</td>\n",
       "      <td>5.42</td>\n",
       "      <td>2.80</td>\n",
       "      <td>66.65</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.98</td>\n",
       "      <td>4091.637316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    Well    Por  Perm    AI  Brittle   TOC    VR         Prod\n",
       "0      1  12.08  2.92  2.80    81.40  1.16  2.31  1695.360819\n",
       "1      2  12.38  3.53  3.22    46.17  0.89  1.88  3007.096063\n",
       "2      3  14.02  2.59  4.01    72.80  0.89  2.72  2531.938259\n",
       "3      4  17.67  6.75  2.63    39.81  1.08  1.88  5288.514854\n",
       "4      5  17.52  4.57  3.18    10.94  1.51  1.90  2859.469624\n",
       "5      6  14.53  4.81  2.69    53.60  0.94  1.67  4017.374438\n",
       "6      7  13.49  3.60  2.93    63.71  0.80  1.85  2952.812773\n",
       "7      8  11.58  3.03  3.25    53.00  0.69  1.93  2670.933846\n",
       "8      9  12.52  2.72  2.43    65.77  0.95  1.98  2474.048178\n",
       "9     10  13.25  3.94  3.71    66.20  1.14  2.65  2722.893266\n",
       "10    11  15.04  4.39  2.22    61.11  1.08  1.77  3828.247174\n",
       "11    12  16.19  6.30  2.29    49.10  1.53  1.86  5095.810104\n",
       "12    13  16.82  5.42  2.80    66.65  1.17  1.98  4091.637316"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data.head(n=13)                                          # preview the first n rows of the DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's remove the well index and check the summary summary statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Por</th>\n",
       "      <td>200.0</td>\n",
       "      <td>14.991150</td>\n",
       "      <td>2.971176</td>\n",
       "      <td>6.550000</td>\n",
       "      <td>12.912500</td>\n",
       "      <td>15.070000</td>\n",
       "      <td>17.402500</td>\n",
       "      <td>23.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>200.0</td>\n",
       "      <td>4.330750</td>\n",
       "      <td>1.731014</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>3.122500</td>\n",
       "      <td>4.035000</td>\n",
       "      <td>5.287500</td>\n",
       "      <td>9.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>200.0</td>\n",
       "      <td>2.968850</td>\n",
       "      <td>0.566885</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>2.547500</td>\n",
       "      <td>2.955000</td>\n",
       "      <td>3.345000</td>\n",
       "      <td>4.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brittle</th>\n",
       "      <td>200.0</td>\n",
       "      <td>48.161950</td>\n",
       "      <td>14.129455</td>\n",
       "      <td>10.940000</td>\n",
       "      <td>37.755000</td>\n",
       "      <td>49.510000</td>\n",
       "      <td>58.262500</td>\n",
       "      <td>84.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOC</th>\n",
       "      <td>200.0</td>\n",
       "      <td>0.990450</td>\n",
       "      <td>0.481588</td>\n",
       "      <td>-0.190000</td>\n",
       "      <td>0.617500</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>2.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VR</th>\n",
       "      <td>200.0</td>\n",
       "      <td>1.964300</td>\n",
       "      <td>0.300827</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>1.770000</td>\n",
       "      <td>1.960000</td>\n",
       "      <td>2.142500</td>\n",
       "      <td>2.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prod</th>\n",
       "      <td>200.0</td>\n",
       "      <td>3864.407081</td>\n",
       "      <td>1553.277558</td>\n",
       "      <td>839.822063</td>\n",
       "      <td>2686.227611</td>\n",
       "      <td>3604.303506</td>\n",
       "      <td>4752.637555</td>\n",
       "      <td>8590.384044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count         mean          std         min          25%  \\\n",
       "Por      200.0    14.991150     2.971176    6.550000    12.912500   \n",
       "Perm     200.0     4.330750     1.731014    1.130000     3.122500   \n",
       "AI       200.0     2.968850     0.566885    1.280000     2.547500   \n",
       "Brittle  200.0    48.161950    14.129455   10.940000    37.755000   \n",
       "TOC      200.0     0.990450     0.481588   -0.190000     0.617500   \n",
       "VR       200.0     1.964300     0.300827    0.930000     1.770000   \n",
       "Prod     200.0  3864.407081  1553.277558  839.822063  2686.227611   \n",
       "\n",
       "                 50%          75%          max  \n",
       "Por        15.070000    17.402500    23.550000  \n",
       "Perm        4.035000     5.287500     9.870000  \n",
       "AI          2.955000     3.345000     4.630000  \n",
       "Brittle    49.510000    58.262500    84.330000  \n",
       "TOC         1.030000     1.350000     2.180000  \n",
       "VR          1.960000     2.142500     2.870000  \n",
       "Prod     3604.303506  4752.637555  8590.384044  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data = my_data.iloc[:,1:]                                # remove the well index\n",
    "my_data.describe().transpose()                              # calculate summary statistics for the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is good that we checked the summary statistics, because we have some negative values for brittleness and total organic carbon. The is physically imposible.  The values must be in error. We know the lowest possible values are 0.0, so we will truncate on 0.0.  We use the *get_numerical_data()* DataFrame member function to get a shallow copy of the data from the DataFrame.  Since it is a shallow copy, any changes we make to the copy are made to the data in the original DataFrame.  This allows us to apply this simple conditional statement to all the data values in the DataFrame all at once.\n",
    "\n",
    "Let's also make a categorical variable for production, based on a threshold of 4,000 MCFPD.\n",
    "\n",
    "* high production > 4,000 MCFPD, cprod = 1\n",
    "* low production <= 4,000 MCFPD, cprod = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Por</th>\n",
       "      <td>200.0</td>\n",
       "      <td>14.991150</td>\n",
       "      <td>2.971176</td>\n",
       "      <td>6.550000</td>\n",
       "      <td>12.912500</td>\n",
       "      <td>15.070000</td>\n",
       "      <td>17.402500</td>\n",
       "      <td>23.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>200.0</td>\n",
       "      <td>4.330750</td>\n",
       "      <td>1.731014</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>3.122500</td>\n",
       "      <td>4.035000</td>\n",
       "      <td>5.287500</td>\n",
       "      <td>9.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>200.0</td>\n",
       "      <td>2.968850</td>\n",
       "      <td>0.566885</td>\n",
       "      <td>1.280000</td>\n",
       "      <td>2.547500</td>\n",
       "      <td>2.955000</td>\n",
       "      <td>3.345000</td>\n",
       "      <td>4.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brittle</th>\n",
       "      <td>200.0</td>\n",
       "      <td>48.161950</td>\n",
       "      <td>14.129455</td>\n",
       "      <td>10.940000</td>\n",
       "      <td>37.755000</td>\n",
       "      <td>49.510000</td>\n",
       "      <td>58.262500</td>\n",
       "      <td>84.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOC</th>\n",
       "      <td>200.0</td>\n",
       "      <td>0.991950</td>\n",
       "      <td>0.478264</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.617500</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>2.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VR</th>\n",
       "      <td>200.0</td>\n",
       "      <td>1.964300</td>\n",
       "      <td>0.300827</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>1.770000</td>\n",
       "      <td>1.960000</td>\n",
       "      <td>2.142500</td>\n",
       "      <td>2.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prod</th>\n",
       "      <td>200.0</td>\n",
       "      <td>3864.407081</td>\n",
       "      <td>1553.277558</td>\n",
       "      <td>839.822063</td>\n",
       "      <td>2686.227611</td>\n",
       "      <td>3604.303506</td>\n",
       "      <td>4752.637555</td>\n",
       "      <td>8590.384044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cProd</th>\n",
       "      <td>200.0</td>\n",
       "      <td>0.435000</td>\n",
       "      <td>0.497001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count         mean          std         min          25%  \\\n",
       "Por      200.0    14.991150     2.971176    6.550000    12.912500   \n",
       "Perm     200.0     4.330750     1.731014    1.130000     3.122500   \n",
       "AI       200.0     2.968850     0.566885    1.280000     2.547500   \n",
       "Brittle  200.0    48.161950    14.129455   10.940000    37.755000   \n",
       "TOC      200.0     0.991950     0.478264    0.000000     0.617500   \n",
       "VR       200.0     1.964300     0.300827    0.930000     1.770000   \n",
       "Prod     200.0  3864.407081  1553.277558  839.822063  2686.227611   \n",
       "cProd    200.0     0.435000     0.497001    0.000000     0.000000   \n",
       "\n",
       "                 50%          75%          max  \n",
       "Por        15.070000    17.402500    23.550000  \n",
       "Perm        4.035000     5.287500     9.870000  \n",
       "AI          2.955000     3.345000     4.630000  \n",
       "Brittle    49.510000    58.262500    84.330000  \n",
       "TOC         1.030000     1.350000     2.180000  \n",
       "VR          1.960000     2.142500     2.870000  \n",
       "Prod     3604.303506  4752.637555  8590.384044  \n",
       "cProd       0.000000     1.000000     1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num = my_data._get_numeric_data()                           # get shallow copy of the numerical values from the DataFrame\n",
    "num[num < 0] = 0                                            # truncate negative values to 0.0\n",
    "my_data['cProd'] = np.where(my_data['Prod']>=4000, 1, 0)    # conditional statement assign a new feature\n",
    "my_data.describe().transpose()                              # calculate summary statistics for the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make sure that we have the new categorical feature for production."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Por</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>Brittle</th>\n",
       "      <th>TOC</th>\n",
       "      <th>VR</th>\n",
       "      <th>Prod</th>\n",
       "      <th>cProd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.08</td>\n",
       "      <td>2.92</td>\n",
       "      <td>2.80</td>\n",
       "      <td>81.40</td>\n",
       "      <td>1.16</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1695.360819</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.38</td>\n",
       "      <td>3.53</td>\n",
       "      <td>3.22</td>\n",
       "      <td>46.17</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.88</td>\n",
       "      <td>3007.096063</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.02</td>\n",
       "      <td>2.59</td>\n",
       "      <td>4.01</td>\n",
       "      <td>72.80</td>\n",
       "      <td>0.89</td>\n",
       "      <td>2.72</td>\n",
       "      <td>2531.938259</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.67</td>\n",
       "      <td>6.75</td>\n",
       "      <td>2.63</td>\n",
       "      <td>39.81</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.88</td>\n",
       "      <td>5288.514854</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.52</td>\n",
       "      <td>4.57</td>\n",
       "      <td>3.18</td>\n",
       "      <td>10.94</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2859.469624</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Por  Perm    AI  Brittle   TOC    VR         Prod  cProd\n",
       "0  12.08  2.92  2.80    81.40  1.16  2.31  1695.360819      0\n",
       "1  12.38  3.53  3.22    46.17  0.89  1.88  3007.096063      0\n",
       "2  14.02  2.59  4.01    72.80  0.89  2.72  2531.938259      0\n",
       "3  17.67  6.75  2.63    39.81  1.08  1.88  5288.514854      1\n",
       "4  17.52  4.57  3.18    10.94  1.51  1.90  2859.469624      0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data.head()                                              # preview the first n rows of the updated DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset has variables from 200 unconventional wells including well average porosity, log transform of permeability (to linearize the relationships with other variables), accoustic impedance (kg/m2s*10^6), brittness ratio (%), total organic carbon (%), vitrinite reflectance (%), and initial production 90 day average (MCFPD).  Note, the dataset is synthetic.\n",
    "\n",
    "#### Calculate the correlation matrix \n",
    "\n",
    "For multivariate analysis it is a good idea to check the correlation matrix.  We can calculate it and view it in the console with these commands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.    0.76 -0.46 -0.22  0.71  0.11  0.88]\n",
      " [ 0.76  1.   -0.24 -0.12  0.47  0.05  0.71]\n",
      " [-0.46 -0.24  1.    0.13 -0.53  0.5  -0.37]\n",
      " [-0.22 -0.12  0.13  1.   -0.21  0.32 -0.02]\n",
      " [ 0.71  0.47 -0.53 -0.21  1.    0.3   0.64]\n",
      " [ 0.11  0.05  0.5   0.32  0.3   1.    0.23]\n",
      " [ 0.88  0.71 -0.37 -0.02  0.64  0.23  1.  ]]\n"
     ]
    }
   ],
   "source": [
    "corr_matrix = np.corrcoef(my_data.iloc[:,:7], rowvar = False)  # correlation matrix without the categorical value\n",
    "print(np.around(corr_matrix,2))                           # print the correlation matrix to 2 decimals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the 1.0 diagonal resulting from the correlation of each variable with themselves.  \n",
    "\n",
    "Let's use our function declared above to make a graphical correlation matrix visualization.  This may inprove our ability to spot features.  It relies on the built in correlation matrix method with Numpy DataFrames and MatPlotLib for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_corr(my_data.iloc[:,:7],10)                            # using our correlation matrix visualization function\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Working with Only Two Features\n",
    "\n",
    "Let's simplify the problem to 2 feature), Porosity and Brittleness to predict Production rate.  By working with only 2 features, it is very easy to visualize the segmentation of the feature space (it is only 2D and can be shown compleltely on a single plot)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_data_subset = my_data.iloc[:,[0,3,7]]                # extract just por, brittle and prod with 100 samples\n",
    "X_train, X_test, y_train, y_test = train_test_split(my_data_subset.iloc[:,[0,1]], my_data_subset.iloc[:,2], test_size=0.25, random_state=73073)\n",
    "y_train = pd.DataFrame({'cprod':y_train.values})\n",
    "y_test = pd.DataFrame({'cprod':y_test.values})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the Min and Max for Plotting \n",
    "\n",
    "x1 and x2 are the predictor features\n",
    "\n",
    "* x1 on x axis and x2 on the y axis for the plots below\n",
    "\n",
    "y is the response feature\n",
    "\n",
    "* used for the color bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1min = 5.0; x1max = 25.0\n",
    "x2min = 0.0; x2max = 100.0\n",
    "ymin = 0.0; ymax = 9000.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first check the univariate statistics of Porosity, Brittleness and Producton."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Por</th>\n",
       "      <td>150.0</td>\n",
       "      <td>15.005267</td>\n",
       "      <td>2.971274</td>\n",
       "      <td>6.55</td>\n",
       "      <td>12.8975</td>\n",
       "      <td>15.055</td>\n",
       "      <td>17.500</td>\n",
       "      <td>23.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brittle</th>\n",
       "      <td>150.0</td>\n",
       "      <td>47.857067</td>\n",
       "      <td>13.886701</td>\n",
       "      <td>10.94</td>\n",
       "      <td>37.8400</td>\n",
       "      <td>49.150</td>\n",
       "      <td>57.985</td>\n",
       "      <td>81.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count       mean        std    min      25%     50%     75%    max\n",
       "Por      150.0  15.005267   2.971274   6.55  12.8975  15.055  17.500  23.55\n",
       "Brittle  150.0  47.857067  13.886701  10.94  37.8400  49.150  57.985  81.40"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.describe().transpose()                         # calculate summary statistics for the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Por</th>\n",
       "      <td>50.0</td>\n",
       "      <td>14.9488</td>\n",
       "      <td>3.00064</td>\n",
       "      <td>7.38</td>\n",
       "      <td>13.3525</td>\n",
       "      <td>15.195</td>\n",
       "      <td>16.9325</td>\n",
       "      <td>20.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brittle</th>\n",
       "      <td>50.0</td>\n",
       "      <td>49.0766</td>\n",
       "      <td>14.94183</td>\n",
       "      <td>15.68</td>\n",
       "      <td>37.3550</td>\n",
       "      <td>52.545</td>\n",
       "      <td>60.0575</td>\n",
       "      <td>84.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count     mean       std    min      25%     50%      75%    max\n",
       "Por       50.0  14.9488   3.00064   7.38  13.3525  15.195  16.9325  20.96\n",
       "Brittle   50.0  49.0766  14.94183  15.68  37.3550  52.545  60.0575  84.33"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.describe().transpose()                         # calculate summary statistics for the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cprod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.433333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            cprod\n",
       "count  150.000000\n",
       "mean     0.433333"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.describe()[:2]                                    # calculate summary statistics for the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cprod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cprod\n",
       "count  50.00\n",
       "mean    0.44"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.describe()[:2]                                    # calculate summary statistics for the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first check the univariate distributions of Porosity, Brittleness and Producton."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(231)\n",
    "plt.hist(X_train[\"Por\"], alpha = 0.8, color = 'darkorange', edgecolor = 'black', bins=20)\n",
    "plt.title('Porosity Training Data (%)')\n",
    "\n",
    "plt.subplot(232)\n",
    "plt.hist(X_train[\"Brittle\"], alpha = 0.8, color = 'darkorange', edgecolor = 'black', bins=20)\n",
    "plt.title('Britteness Training Data (%)')\n",
    "\n",
    "plt.subplot(233)\n",
    "plt.hist(y_train['cprod'], alpha = 0.8, color = 'darkorange', edgecolor = 'black', bins=20)\n",
    "plt.title('Production Training Data (MCFPD)')\n",
    "\n",
    "plt.subplot(234)\n",
    "plt.hist(X_test[\"Por\"], alpha = 0.8, color = 'darkorange', edgecolor = 'black', bins=20)\n",
    "plt.title('Porosity Testing Data (%)')\n",
    "\n",
    "plt.subplot(235)\n",
    "plt.hist(X_test[\"Brittle\"], alpha = 0.8, color = 'darkorange', edgecolor = 'black', bins=20)\n",
    "plt.title('Britteness Testing Data (%)')\n",
    "\n",
    "plt.subplot(236)\n",
    "plt.hist(y_test['cprod'], alpha = 0.8, color = 'darkorange', edgecolor = 'black', bins=20)\n",
    "plt.title('Production Testing Data (MCFPD)')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=3.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distributions are well behaved, we cannot observe obvious gaps nor truncations.  Let's look at a scatter plot of Porosity vs. Brittleness with points colored by Production."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "im = plt.scatter(X_train[\"Por\"],X_train[\"Brittle\"],s=None, c=y_train['cprod'], marker=None, cmap=cmap, norm=None, vmin=None, vmax=None, alpha=0.8, linewidths=0.3, edgecolors=\"black\")\n",
    "plt.title('Training Production vs. Brittleness and Porosity'); plt.xlabel('Porosity (%)'); plt.ylabel('Brittleness (%)')\n",
    "cbar = plt.colorbar(im, orientation = 'vertical')\n",
    "cbar.set_label(\"Production\", rotation=270, labelpad=20)\n",
    "\n",
    "plt.subplot(122)\n",
    "im = plt.scatter(X_test[\"Por\"],X_test[\"Brittle\"],s=None, c=y_test['cprod'], marker=None, cmap=cmap, norm=None, vmin=None, vmax=None, alpha=0.8, linewidths=0.3, edgecolors=\"black\")\n",
    "plt.title('Testing Production vs. Brittleness and Porosity'); plt.xlabel('Porosity (%)'); plt.ylabel('Brittleness (%)')\n",
    "cbar = plt.colorbar(im, orientation = 'vertical')\n",
    "cbar.set_label(\"Production\", rotation=270, labelpad=20)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instantiate, Fit and Predict with Gausian Naive Bayes\n",
    "\n",
    "Let's build a Gaussian naive Bayes model.\n",
    "\n",
    "We select the Gaussian model as it simplifies the inference problem to just a set of conditional means and variances given each feature.\n",
    "\n",
    "Recall we can set a prior probability of each response category\n",
    "\n",
    "* We will use the proportions from the training dataset.\n",
    "\n",
    "* 0.43 for high production (the mean of the binary dataset is the proportion of 1's)\n",
    "\n",
    "* 0.57 for low production (1 - proportion of high production)\n",
    "\n",
    "Another option would be to assume a naive, uniform prior, substitute the following:\n",
    "\n",
    "```python\n",
    "priors = (0.5,0.5)   # naive prior\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "priors = (0.5,0.5)                                        # set the prior probabilities of low and high production"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's build our Gaussian naive Bayes model.\n",
    "\n",
    "* instantiate it with the priors \n",
    "\n",
    "* train with the training data, we use the standard fit function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB(priors = priors)                           # instantiate the Gaussian naive Bayes model\n",
    "GaussianNB_fit = gnb.fit(X_train,y_train['cprod'])   # train with the training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's predict with our new model over the testing dataset.\n",
    "\n",
    "* test by predicting with the testing data, we use the standard prediction function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = GaussianNB_fit.predict(np.c_[X_test['Por'],X_test['Brittle']]) # predict over the testing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Checking\n",
    "\n",
    "Let's check our model. With scikit learn we have great built in tools to evaluate our classification model. Let's try the classification report first.\n",
    "\n",
    "```python\n",
    "classification_report(truth, predicted)                # build a classification report to check our classification model\n",
    "```\n",
    "\n",
    "We get a table with summary metrics for model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.96      0.96        28\n",
      "           1       0.95      0.95      0.95        22\n",
      "\n",
      "    accuracy                           0.96        50\n",
      "   macro avg       0.96      0.96      0.96        50\n",
      "weighted avg       0.96      0.96      0.96        50\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test['cprod'].values, y_pred, labels=[0,1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The metrics include:\n",
    "    \n",
    "    * recall - the ratio of true positives divided by all cases of the category in the testing dataset\n",
    "    \n",
    "    * precision - the ratio of true positives divided by all positives (true positives + false positives)\n",
    "    \n",
    "    * f1-score - the harmonic mean of recall and precision\n",
    "    \n",
    "    * support - the number of samples of each category in the testing data\n",
    "    \n",
    "I also like to look at the confusion matrix.  \n",
    "\n",
    "* the x axis is the prediction - category 0 or 1\n",
    "\n",
    "* the y axis is the truth - category 0 or 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[27  1]\n",
      " [ 1 21]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_test['cprod'].values, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above we can observe:\n",
    "\n",
    "* 26 low production wells classified correctly as low production\n",
    "\n",
    "* 1 high production well misclassified as low production\n",
    "\n",
    "* 2 low production wells misclassified as high production\n",
    "\n",
    "* 21 high production wells classified correctly as high production\n",
    "\n",
    "#### Visualizing the Classification Model\n",
    "\n",
    "Let's visualize the model over the entire feature space.\n",
    "\n",
    "* here's the training data with the classification over the full range of predictor features.\n",
    "\n",
    "* blue for low production and yellow for high production\n",
    "\n",
    "Note: naive Bayes provides the posterior probability of high and low production\n",
    "\n",
    "* the classifications below are based on maximum apriori selection (MAPS), selecting the category with the highest probability\n",
    "\n",
    "Let's visualize the classification model (blue - low production, yellow - high production) over the predictor feature space with the training data plotted (white - low production, black - high production)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\pm27995\\\\Anaconda3\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model(GaussianNB_fit,X_train[\"Por\"],x1min,x1max,X_train[\"Brittle\"],x2min,x2max,y_train['cprod'],0.0,1.0,'Training Data and Naive Bayes Model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also visualize the posterior probabilities of low and high production.\n",
    "\n",
    "* here's the posterior probability of low and high production over the predictor feature space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model_prob(GaussianNB_fit,X_train[\"Por\"],x1min,x1max,X_train[\"Brittle\"],x2min,x2max,y_train['cprod'],'Training Data and Naive Bayes Model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's look at the classification model over the predictor feature space (blue - low production, yellow - high production) with the testing data plotted (white - low production, black - high production)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "visualize_model(GaussianNB_fit,X_test[\"Por\"],x1min,x1max,X_test[\"Brittle\"],x2min,x2max,y_test['cprod'],0.0,1.0,'Testing Data and Naive Bayes Model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a reasonable model to predict well production from porosity and brittleness for an unconventional reservoir.\n",
    "\n",
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of naive Bayes for prediction. A lot more could be done, for example, we could have applied variants such as:\n",
    "\n",
    "* multinomial naive Bayes\n",
    "\n",
    "* compliment naive Bayes\n",
    "\n",
    "* Bernoulli naive Bayes\n",
    "\n",
    "We could have worked with more predictor features, but for learning the method, it is nice to be able to visualize the entire classification in one plot!\n",
    "\n",
    "If you struggled with the basic Python used here check out my other basic demonstrations for DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations, trend modeling and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations, trend modeling and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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